Multiple animal tracking based on deep learning is a crucial and demanding task with
extensive applications in agriculture, livestock farming, and ecological research. The primary …

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …

Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that
objects move linearly. While this assumption is acceptable for very short periods of …

In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end
multi-object tracking with a pretrained object detector. Existing end-to-end methods, eg …

A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization,
and following re-identification (re-ID) for object association. This pipeline is partially …

Multi-object tracking (MOT) in sports scenes plays a critical role in gathering players
statistics, supporting further applications, such as automatic tactical analysis. Yet existing …

We introduce a novel paradigm for offline Video Instance Segmentation (VIS), based on the
hypothesis that explicit object-oriented information can be a strong clue for understanding …

The ability to recognize, localize and track dynamic objects in a scene is fundamental to
many real-world applications, such as self-driving and robotic systems. Yet, traditional …

Exploring robust and efficient association methods has always been an important issue in
multi-object tracking (MOT). Although existing tracking methods have achieved impressive …

Abstract We propose a Cascaded Buffered IoU (C-BIoU) tracker to track multiple objects that
have irregular motions and indistinguishable appearances. When appearance features are …